Academic Publications: ACL Anthology (page 3)
Academic Publications · ACL Anthology: 52 entries in total; this page lists entries 31–45.
Mapping 1,000+ Language Models via the Log-Likelihood Vector
Evidence grade: for discovery only; must not by itself support a formal conclusion.
The paper concerns foundation models and model architecture. Its ACL 2025 Outstanding Paper award is verified, but independent external evidence of a major contribution to AI is still missing, so it is currently listed only as an entry awaiting further evidence.
Evidence use: For checking the paper's identity, authors, official award and original content; currently only a discovery and evidence-gathering entry point, not evidence of a major AI contribution confirmed by this site.
Limitations: Being listed publicly records a gap in the review; it does not mean the importance review was passed, nor that this site endorses all of the paper's conclusions. The official award shows recognition under that award process; until independent evidence of major contribution is added, it cannot be claimed that the paper significantly changed AI.
Related concepts: Large Language Model (LLM) · Transformer
Link last checked: 2026-09-24
MauBERT: Universal Phonetic Inductive Biases for Few-Shot Acoustic Units Discovery
Evidence grade: for discovery only; must not by itself support a formal conclusion.
The paper concerns computational linguistics and language technology. Its ACL 2026 Outstanding Paper Award is verified, but independent external evidence of a major contribution to AI is still missing, so it is currently listed only as an entry awaiting further evidence.
Evidence use: For checking the paper's identity, authors, official award and original content; currently only a discovery and evidence-gathering entry point, not evidence of a major AI contribution confirmed by this site.
Limitations: Being listed publicly records a gap in the review; it does not mean the importance review was passed, nor that this site endorses all of the paper's conclusions. The official award shows recognition under that award process; until independent evidence of major contribution is added, it cannot be claimed that the paper significantly changed AI.
Link last checked: 2026-09-24
Evolutionary Guided Decoding: Iterative Value Refinement for LLMs
Evidence grade: for discovery only; must not by itself support a formal conclusion.
The paper concerns foundation models and model architecture, and evaluation, reliability and safety. Its ACL 2026 Outstanding Paper Award is verified, but independent external evidence of a major contribution to AI is still missing, so it is currently listed only as an entry awaiting further evidence.
Evidence use: For checking the paper's identity, authors, official award and original content; currently only a discovery and evidence-gathering entry point, not evidence of a major AI contribution confirmed by this site.
Limitations: Being listed publicly records a gap in the review; it does not mean the importance review was passed, nor that this site endorses all of the paper's conclusions. The official award shows recognition under that award process; until independent evidence of major contribution is added, it cannot be claimed that the paper significantly changed AI.
Related concepts: Large Language Model (LLM) · Transformer · Model Evaluation and Benchmarks
Link last checked: 2026-09-24
Beyond the Final Actor: Modeling the Dual Roles of Creator and Editor for Fine-Grained LLM-Generated Text Detection
Evidence grade: for discovery only; must not by itself support a formal conclusion.
The paper concerns foundation models and model architecture. Its ACL 2026 Outstanding Paper Award is verified, but independent external evidence of a major contribution to AI is still missing, so it is currently listed only as an entry awaiting further evidence.
Evidence use: For checking the paper's identity, authors, official award and original content; currently only a discovery and evidence-gathering entry point, not evidence of a major AI contribution confirmed by this site.
Limitations: Being listed publicly records a gap in the review; it does not mean the importance review was passed, nor that this site endorses all of the paper's conclusions. The official award shows recognition under that award process; until independent evidence of major contribution is added, it cannot be claimed that the paper significantly changed AI.
Related concepts: Large Language Model (LLM) · Transformer
Link last checked: 2026-09-24
Lying with Truths: Open-Channel Multi-Agent Collusion for Belief Manipulation via Generative Montage
Evidence grade: for discovery only; must not by itself support a formal conclusion.
The paper concerns foundation models and model architecture. Its ACL 2026 Outstanding Paper Award is verified, but independent external evidence of a major contribution to AI is still missing, so it is currently listed only as an entry awaiting further evidence.
Evidence use: For checking the paper's identity, authors, official award and original content; currently only a discovery and evidence-gathering entry point, not evidence of a major AI contribution confirmed by this site.
Limitations: Being listed publicly records a gap in the review; it does not mean the importance review was passed, nor that this site endorses all of the paper's conclusions. The official award shows recognition under that award process; until independent evidence of major contribution is added, it cannot be claimed that the paper significantly changed AI.
Related concepts: Large Language Model (LLM) · Transformer
Link last checked: 2026-09-24
Hierarchical Acoustic-Semantic Modeling: Modality Separation and Semantic Coherence for Full-Duplex SLMs
Evidence grade: for discovery only; must not by itself support a formal conclusion.
The paper concerns foundation models and model architecture; evaluation, reliability and safety; and training and inference efficiency and infrastructure. Its ACL 2026 Outstanding Paper Award is verified, but independent external evidence of a major contribution to AI is still missing, so it is currently listed only as an entry awaiting further evidence.
Evidence use: For checking the paper's identity, authors, official award and original content; currently only a discovery and evidence-gathering entry point, not evidence of a major AI contribution confirmed by this site.
Limitations: Being listed publicly records a gap in the review; it does not mean the importance review was passed, nor that this site endorses all of the paper's conclusions. The official award shows recognition under that award process; until independent evidence of major contribution is added, it cannot be claimed that the paper significantly changed AI.
Related concepts: Large Language Model (LLM) · Transformer · Model Evaluation and Benchmarks · LLM Inference Optimization
Link last checked: 2026-09-24
Mind the (DH) Gap! A Contrast in Risky Choices Between Reasoning and Conversational LLMs
Evidence grade: for discovery only; must not by itself support a formal conclusion.
The paper concerns foundation models and model architecture, and retrieval, agents and memory. Its ACL 2026 Outstanding Paper Award is verified, but independent external evidence of a major contribution to AI is still missing, so it is currently listed only as an entry awaiting further evidence.
Evidence use: For checking the paper's identity, authors, official award and original content; currently only a discovery and evidence-gathering entry point, not evidence of a major AI contribution confirmed by this site.
Limitations: Being listed publicly records a gap in the review; it does not mean the importance review was passed, nor that this site endorses all of the paper's conclusions. The official award shows recognition under that award process; until independent evidence of major contribution is added, it cannot be claimed that the paper significantly changed AI.
Related concepts: Large Language Model (LLM) · Transformer · Retrieval-Augmented Generation (RAG) · AI Agent
Link last checked: 2026-09-24
The Imperfective Paradox in Large Language Models
Evidence grade: for discovery only; must not by itself support a formal conclusion.
The paper concerns foundation models and model architecture, and evaluation, reliability and safety. Its ACL 2026 Best Paper Award is verified, but there is an unresolved conflict in key evidence, so it is currently listed only as an entry awaiting re-check.
Evidence use: For checking the paper's identity, authors, official award and original content; currently only a discovery and evidence-gathering entry point, not evidence of a major AI contribution confirmed by this site.
Limitations: Being listed publicly records a gap in the review; it does not mean the importance review was passed, nor that this site endorses all of the paper's conclusions. The official award is confirmed, but a conflict in key evidence is unresolved; criticism should not be taken to mean the paper has been refuted or the award withdrawn.
Related concepts: Large Language Model (LLM) · Transformer · Model Evaluation and Benchmarks
Link last checked: 2026-09-24
MediEval: A Unified Medical Benchmark for Patient-Contextual and Knowledge-Grounded Reasoning in LLMs
Evidence grade: for discovery only; must not by itself support a formal conclusion.
The paper concerns foundation models and model architecture. Its ACL 2026 Outstanding Paper Award is verified, but independent external evidence of a major contribution to AI is still missing, so it is currently listed only as an entry awaiting further evidence.
Evidence use: For checking the paper's identity, authors, official award and original content; currently only a discovery and evidence-gathering entry point, not evidence of a major AI contribution confirmed by this site.
Limitations: Being listed publicly records a gap in the review; it does not mean the importance review was passed, nor that this site endorses all of the paper's conclusions. The official award shows recognition under that award process; until independent evidence of major contribution is added, it cannot be claimed that the paper significantly changed AI.
Related concepts: Large Language Model (LLM) · Transformer
Link last checked: 2026-09-24
Maximizing Local Entropy Where It Matters: Prefix-Aware Localized LLM Unlearning
Evidence grade: for discovery only; must not by itself support a formal conclusion.
The paper concerns foundation models and model architecture, and evaluation, reliability and safety. Its ACL 2026 Outstanding Paper Award is verified, but independent external evidence of a major contribution to AI is still missing, so it is currently listed only as an entry awaiting further evidence.
Evidence use: For checking the paper's identity, authors, official award and original content; currently only a discovery and evidence-gathering entry point, not evidence of a major AI contribution confirmed by this site.
Limitations: Being listed publicly records a gap in the review; it does not mean the importance review was passed, nor that this site endorses all of the paper's conclusions. The official award shows recognition under that award process; until independent evidence of major contribution is added, it cannot be claimed that the paper significantly changed AI.
Related concepts: Large Language Model (LLM) · Transformer · Model Evaluation and Benchmarks
Link last checked: 2026-09-24
GeoRA: Geometry-Aware Low-Rank Adaptation for RLVR
Evidence grade: for discovery only; must not by itself support a formal conclusion.
The paper concerns training, alignment and reasoning, and training and inference efficiency and infrastructure. Its ACL 2026 Outstanding Paper Award is verified, but independent external evidence of a major contribution to AI is still missing, so it is currently listed only as an entry awaiting further evidence.
Evidence use: For checking the paper's identity, authors, official award and original content; currently only a discovery and evidence-gathering entry point, not evidence of a major AI contribution confirmed by this site.
Limitations: Being listed publicly records a gap in the review; it does not mean the importance review was passed, nor that this site endorses all of the paper's conclusions. The official award shows recognition under that award process; until independent evidence of major contribution is added, it cannot be claimed that the paper significantly changed AI.
Related concepts: Fine-tuning · Reasoning Models · LLM Inference Optimization
Link last checked: 2026-09-24
CURE: Critique-Driven Unified Reinforcement Learning for Test-Time Self-Improvement
Evidence grade: for discovery only; must not by itself support a formal conclusion.
The paper concerns foundation models and model architecture, and training, alignment and reasoning. Its ACL 2026 Outstanding Paper Award is verified, but independent external evidence of a major contribution to AI is still missing, so it is currently listed only as an entry awaiting further evidence.
Evidence use: For checking the paper's identity, authors, official award and original content; currently only a discovery and evidence-gathering entry point, not evidence of a major AI contribution confirmed by this site.
Limitations: Being listed publicly records a gap in the review; it does not mean the importance review was passed, nor that this site endorses all of the paper's conclusions. The official award shows recognition under that award process; until independent evidence of major contribution is added, it cannot be claimed that the paper significantly changed AI.
Related concepts: Large Language Model (LLM) · Transformer · Fine-tuning · Reasoning Models
Link last checked: 2026-09-24
Systematicity between Forms and Meanings across Languages Supports Efficient Communication
Evidence grade: for discovery only; must not by itself support a formal conclusion.
The paper concerns computational linguistics and language technology. Its ACL 2026 Outstanding Paper Award is verified, but independent external evidence of a major contribution to AI is still missing, so it is currently listed only as an entry awaiting further evidence.
Evidence use: For checking the paper's identity, authors, official award and original content; currently only a discovery and evidence-gathering entry point, not evidence of a major AI contribution confirmed by this site.
Limitations: Being listed publicly records a gap in the review; it does not mean the importance review was passed, nor that this site endorses all of the paper's conclusions. The official award shows recognition under that award process; until independent evidence of major contribution is added, it cannot be claimed that the paper significantly changed AI.
Link last checked: 2026-09-24
Rethinking Entropy Interventions in RLVR: An Entropy Change Perspective
Evidence grade: for discovery only; must not by itself support a formal conclusion.
The paper concerns foundation models and model architecture, and training, alignment and reasoning. Its ACL 2026 Outstanding Paper Award is verified, but independent external evidence of a major contribution to AI is still missing, so it is currently listed only as an entry awaiting further evidence.
Evidence use: For checking the paper's identity, authors, official award and original content; currently only a discovery and evidence-gathering entry point, not evidence of a major AI contribution confirmed by this site.
Limitations: Being listed publicly records a gap in the review; it does not mean the importance review was passed, nor that this site endorses all of the paper's conclusions. The official award shows recognition under that award process; until independent evidence of major contribution is added, it cannot be claimed that the paper significantly changed AI.
Related concepts: Large Language Model (LLM) · Transformer · Fine-tuning · Reasoning Models
Link last checked: 2026-09-24
Memory efficiency and resource-rational encoding in sentence processing
Evidence grade: for discovery only; must not by itself support a formal conclusion.
The paper concerns foundation models and model architecture, and evaluation, reliability and safety. Its ACL 2026 Best Paper Award is verified, but independent external evidence of a major contribution to AI is still missing, so it is currently listed only as an entry awaiting further evidence.
Evidence use: For checking the paper's identity, authors, official award and original content; currently only a discovery and evidence-gathering entry point, not evidence of a major AI contribution confirmed by this site.
Limitations: Being listed publicly records a gap in the review; it does not mean the importance review was passed, nor that this site endorses all of the paper's conclusions. The official award shows recognition under that award process; until independent evidence of major contribution is added, it cannot be claimed that the paper significantly changed AI.
Related concepts: Large Language Model (LLM) · Transformer · Model Evaluation and Benchmarks
Link last checked: 2026-09-24